Building a large-scale Data as a Service (DaaS) platform to consistently deliver high-quality datasets
Submitted by Aayushi Pathak (@09aayushi) (proposing) on Wednesday, 1 May 2019
This is a proposal requesting for someone to speak on this topic. If you’d like to speak, leave a comment.
Session type: Short talk of 20 mins
As a provider of Competitive Intelligence as a Service to eCommerce businesses and consumer
brands, DataWeave aggregates and analyses product catalog data from eCommerce websites each
day at massive scale. Once aggregated, this data is fed into a complex process of extraction,
transformation, machine learning, and analyses. These operations are performed on a consistent
basis to provide our customers with easily consumable and actionable insights.
To be precise, we aggregate over 200 million data points across 2000+ web sources to deliver 200+
reports each day.
The web sources span multiple verticals ranging across eCommerce, travel, classified listings, mobile
apps, and more. The data transformation may range from a simple ETL to more complex ML models.
Having a unified framework to collect, transform, analyze, validate and deliver the data at a large
scale is a significant challenge.
As organizations continue to invest in and consume Big Data, challenges in capturing, structuring, and processing data in a meaningful and cost-effective manner are growing causes for concern. The scope of the problem is wide-ranging, especially since Big Data is now mission-critical in diverse industries, such as healthcare, banking, IoT, retail, and more.
Smart organizations are looking for ways to capture and store data (both internal and third-party) at scale, process it efficiently, and generate actionable insights consistently – all without reaching for deeper pockets.
This talk will throw some light on DataWeave’s journey in building a data processing framework which handles diverse datasets at scale and delivers accurate and high-quality insights consistently to its retail customers.
Evolution of the platform
1. Data Collection at scale
Managing the challenges to collect publicly available data
2. Data processing
3. Data Delivery
Data validation and retry
Delivering custom reports
4. Airflow: For scheduling the entire pipeline
Rahul Ramesh, Architect, DataWeave
I work as an architect in the data platforms team at DataWeave, a provider of Competitive Intelligence as a Service for eCommerce businesses and consumer brands. I design and manage dataflows to various ‘Datastores’ maintained by the company. I also ensure that all datastores are working at optimum capacity, and data consistency is maintained across them.
I have more than 12 years of experience in the software industry, with extensive experience in building core networks in the telecommunications domain. I hold a master’s degree from IIIT-Bangalore.